Mental Stress Detection in Students Using Machine Learning and Behavioral Analysis

Project Synopsis & Overview

This project develops a machine learning-based system for detecting mental stress levels among university students using physiological and behavioral data. Random Forest, Logistic Regression, and Support Vector Machine algorithms are trained and evaluated for stress classification. A Flask-based web application allows users to provide relevant information and receive stress-level predictions, supporting early awareness and proactive mental well-being management in academic environments.

Complete Technical Specifications